EE 381K-6: Estimation Theory

Level: Graduate

Introduction to the fundamentals of estimation theory, with applications to stochastic and adaptive signal processing. Topics include deterministic and stochastic least-squares estimation; the innovation process; spectral factorization and Wiener filtering; state-space structure and Kalman filters; array and fast array algorithms; LMS and RLS adaptive filters; Markov chain Monte Carlo methods; Bayesian filtering and particle filters; parameter estimation; expectation-maximization algorithm; Cramer-Rao bounds.